For the past year, most of my client conversations have focused on managing AI costs. FinOps teams were focused on tracking token consumption, comparing model pricing, allocating AI spend, and identifying ways to reduce expenses. Those are still important priorities, but recent capacity constraint announcements from AWS , Azure , Google Cloud , and Oracle Cloud highlight a larger shift taking place in FinOps. When AI infrastructure is the limiting factor to innovation, FinOps becomes less about reducing costs and more about governing scarcity. The critical challenge shifts from optimization to deciding who gets access to limited capacity, when, and for what business initiative.

Traditional practice is to schedule resources and eliminate waste, but capacity shortages have led to organizations holding or reserving massive amounts of idle GPUs because the risk of losing access outweighed the cost of carrying unused resources.…